Overview: Computer vision and machine learning for microstructural characterization and analysis

• URL: http://arxiv.org/abs/2005.14260v1
• Date: Thu, 28 May 2020 19:51:23 GMT
• Title: Overview: Computer vision and machine learning for microstructural characterization and analysis
• Authors: Elizabeth A. Holm, Ryan Cohn, Nan Gao, Andrew R. Kitahara, Thomas P. Matson, Bo Lei, Srujana Rao Yarasi
• Abstract summary: Computer vision (CV) and machine learning (ML) offer new approaches to extracting information from microstructural images. CV/ML systems for microstructural characterization and analysis span the taxonomy of image analysis tasks. These tools enable new approaches to microstructural analysis, including the development of new, rich visual metrics.
• Score: 1.4194966466632493
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The characterization and analysis of microstructure is the foundation of microstructural science, connecting the materials structure to its composition, process history, and properties. Microstructural quantification traditionally involves a human deciding a priori what to measure and then devising a purpose-built method for doing so. However, recent advances in data science, including computer vision (CV) and machine learning (ML) offer new approaches to extracting information from microstructural images. This overview surveys CV approaches to numerically encode the visual information contained in a microstructural image, which then provides input to supervised or unsupervised ML algorithms that find associations and trends in the high-dimensional image representation. CV/ML systems for microstructural characterization and analysis span the taxonomy of image analysis tasks, including image classification, semantic segmentation, object detection, and instance segmentation. These tools enable new approaches to microstructural analysis, including the development of new, rich visual metrics and the discovery of processing-microstructure-property relationships.

Related papers

• Hierarchical Network Fusion for Multi-Modal Electron Micrograph Representation Learning with Foundational Large Language Models [0.0]
  We propose an innovative backbone architecture for analyzing electron micrographs. We create multi-modal representations of the micrographs by tokenizing them into patch sequences and, additionally, representing them as vision graphs. Our framework outperforms traditional methods, overcoming challenges posed by distributional shifts.
  arXiv  Detail & Related papers  (2024-08-24T19:24:44Z)
• Masked Image Modeling: A Survey [73.21154550957898]
  Masked image modeling emerged as a powerful self-supervised learning technique in computer vision. We construct a taxonomy and review the most prominent papers in recent years. We aggregate the performance results of various masked image modeling methods on the most popular datasets.
  arXiv  Detail & Related papers  (2024-08-13T07:27:02Z)
• Learning Correlation Structures for Vision Transformers [93.22434535223587]
  We introduce a new attention mechanism, dubbed structural self-attention (StructSA) We generate attention maps by recognizing space-time structures of key-query correlations via convolution. This effectively leverages rich structural patterns in images and videos such as scene layouts, object motion, and inter-object relations.
  arXiv  Detail & Related papers  (2024-04-05T07:13:28Z)
• From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Models [98.41645229835493]
  Data visualization in the form of charts plays a pivotal role in data analysis, offering critical insights and aiding in informed decision-making. Large foundation models, such as large language models, have revolutionized various natural language processing tasks. This survey paper serves as a comprehensive resource for researchers and practitioners in the fields of natural language processing, computer vision, and data analysis.
  arXiv  Detail & Related papers  (2024-03-18T17:57:09Z)
• MatSAM: Efficient Extraction of Microstructures of Materials via Visual Large Model [11.130574172301365]
  Segment Anything Model (SAM) is a large visual model with powerful deep feature representation and zero-shot generalization capabilities. In this paper, we propose MatSAM, a general and efficient microstructure extraction solution based on SAM. A simple yet effective point-based prompt generation strategy is designed, grounded on the distribution and shape of microstructures.
  arXiv  Detail & Related papers  (2024-01-11T03:18:18Z)
• Computer Vision Methods for the Microstructural Analysis of Materials: The State-of-the-art and Future Perspectives [0.4595477728342621]
  This review paper focuses on the state-of-the-art CNN-based techniques that have been applied to various multi-scale microstructural image analysis tasks. We identify the main challenges with regard to the application of these methods to materials science research.
  arXiv  Detail & Related papers  (2022-07-29T15:27:47Z)
• Three-dimensional microstructure generation using generative adversarial neural networks in the context of continuum micromechanics [77.34726150561087]
  This work proposes a generative adversarial network tailored towards three-dimensional microstructure generation. The lightweight algorithm is able to learn the underlying properties of the material from a single microCT-scan without the need of explicit descriptors.
  arXiv  Detail & Related papers  (2022-05-31T13:26:51Z)
• Digital Fingerprinting of Microstructures [44.139970905896504]
  Finding efficient means of fingerprinting microstructural information is a critical step towards harnessing data-centric machine learning approaches. Here, we consider microstructure classification and utilise the resulting features over a range of related machine learning tasks. In particular, methods that leverage transfer learning with convolutional neural networks (CNNs), pretrained on the ImageNet dataset, are generally shown to outperform other methods.
  arXiv  Detail & Related papers  (2022-03-25T15:40:44Z)
• Video-based Facial Micro-Expression Analysis: A Survey of Datasets, Features and Algorithms [52.58031087639394]
  micro-expressions are involuntary and transient facial expressions. They can provide important information in a broad range of applications such as lie detection, criminal detection, etc. Since micro-expressions are transient and of low intensity, their detection and recognition is difficult and relies heavily on expert experiences.
  arXiv  Detail & Related papers  (2022-01-30T05:14:13Z)
• Graph signal processing for machine learning: A review and new perspectives [57.285378618394624]
  We review a few important contributions made by GSP concepts and tools, such as graph filters and transforms, to the development of novel machine learning algorithms. We discuss exploiting data structure and relational priors, improving data and computational efficiency, and enhancing model interpretability. We provide new perspectives on future development of GSP techniques that may serve as a bridge between applied mathematics and signal processing on one side, and machine learning and network science on the other.
  arXiv  Detail & Related papers  (2020-07-31T13:21:33Z)
• Image-driven discriminative and generative machine learning algorithms for establishing microstructure-processing relationships [0.49259062564301753]
  We develop an improved machine learning approach to image recognition, characterization, and building predictive capabilities. A binary alloy (uranium-molybdenum) that is currently under development as a nuclear fuel was studied. A F1 score of 95.1% was achieved for distinguishing between micrographs corresponding to ten different thermo-mechanical material processing conditions.
  arXiv  Detail & Related papers  (2020-07-27T10:36:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.